Search

The substantial increase in deployment of customer-sited solar photovoltaics (PV) in the United States has been driven by a combination of steeply declining costs, financing innovations, and supportive policies. Among those supportive policies is net metering, which in most states effectively allows customers to receive compensation for distributed PV generation at the full retail electricity price. The current design of retail electricity rates and the presence of net metering have elicited concerns that the possible under-recovery of fixed utility costs from PV system owners may lead to a feedback loop of increasing retail prices that accelerate PV adoption and further rate increases. However, a separate and opposing feedback loop could offset this effect: increased PV deployment may lead to a shift in the timing of peak-period electricity prices that could reduce the bill savings received under net metering where time-varying retail electricity rates are used, thereby dampening further PV adoption. In this paper, we examine the impacts of these two competing feedback dynamics on U.S. distributed PV deployment through 2050 for both residential and commercial customers, across states. Our results indicate that, at the aggregate national level, the two feedback effects nearly offset one another and therefore produce a modest net effect, although their magnitude and direction vary by customer segment and by state. We also model aggregate PV deployment trends under various rate designs and net-metering rules, accounting for feedback dynamics. Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures. Whereas flat, time-invariant rates with net metering lead to higher aggregate national deployment levels than the current mix of rate structures (+5% in 2050), rate structures with higher monthly fixed customer charges or PV compensation at levels lower than the full retail rate can dramatically erode aggregate customer adoption of PV (from −14% to −61%, depending on the design). Moving towards time-varying rates, on the other hand, accelerates near- and medium-term deployment (through 2030) but slows adoption in the longer term (−22% in 2050). [ABSTRACT FROM AUTHOR]